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Статті в журналах з теми "XGBOOST MODEL"

Автор: Grafiati

Опубліковано: 16 січня 2024

Оновлено: 25 липня 2025

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1

Zeng, Fanchao, Qing Gao, Lifeng Wu, et al. "Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model." Atmosphere 16, no. 4 (2025): 419. https://doi.org/10.3390/atmos16040419.

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Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), (2) a feature-optimized XGBoost variant incorporating Pearson correlation analysis (XGBoost2), and (3) an enhanced CPSO-XGBoost model integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection and parameter t

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2

Zhao, Tianwen, Guoqing Chen, Sujitta Suraphee, Tossapol Phoophiwfa, and Piyapatr Busababodhin. "A hybrid TCN-XGBoost model for agricultural product market price forecasting." PLOS One 20, no. 5 (2025): e0322496. https://doi.org/10.1371/journal.pone.0322496.

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Price volatility in agricultural markets is influenced by seasonality, supply-demand fluctuations, policy changes, and climate. These factors significantly impact agricultural production and the broader macroeconomy. Traditional time series models, limited by linear assumptions, often fail to capture the nonlinear nature of price fluctuations. To address this limitation, we propose an integrated forecasting model that combines TCN and XGBoost to improve the accuracy of agricultural price volatility predictions. TCN captures both short-term and long-term dependencies using convolutional operati

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3

Nabilah Selayanti, Dwi Amalia Putri, Trimono Trimono, and Mohammad Idhom. "PREDIKSI HARGA PENUTUPAN SAHAM BBRI DENGAN MODEL HYBRID LSTM-XGBOOST." Informatika: Jurnal Teknik Informatika dan Multimedia 5, no. 1 (2025): 52–64. https://doi.org/10.51903/informatika.v5i1.1011.

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The ease of investing in the digital era has driven Generation Z to dominate stock market participation, particularly in blue-chip stocks such as PT Bank Rakyat Indonesia Tbk (BBRI). However, stock price fluctuations influenced by macroeconomic factors, regulations, and global market sentiment make it difficult for investors to make accurate decisions. Decisions based on insufficient information pose a significant risk of loss, especially for novice investors. This study proposes a hybrid LSTM-XGBoost approach for predicting BBRI stock prices, combining the strengths of LSTM in capturing nonli

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4

OUKHOUYA, HASSAN, HAMZA KADIRI, KHALID EL HIMDI, and RABY GUERBAZ. "Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models." Statistics, Optimization & Information Computing 12, no. 1 (2023): 200–209. http://dx.doi.org/10.19139/soic-2310-5070-1822.

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   Forecasting time series is crucial for financial research and decision-making in business. The nonlinearity of stock market prices profoundly impacts global economic and financial sectors. This study focuses on modeling and forecasting the daily prices of key stock indices - MASI, CAC 40, DAX, FTSE 250, NASDAQ, and HKEX, representing the Moroccan, French, German, British, US, and Hong Kong markets, respectively. We compare the performance of machine learning models, including Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), and the hybrid LSTM-XGBoost, a

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5

Tran, Thanh-Ngoc, and Quoc-Dai Nguyen. "Research on the Influence of Genetic Algorithm Parameters on XGBoost in Load Forecasting." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18849–54. https://doi.org/10.48084/etasr.8863.

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Electric load forecasting is crucial in a power system comprising electricity generation, transmission, distribution, and retail. Due to its high accuracy, the ensemble learning method XGBoost has been widely applied in load forecasting. XGBoost's performance depends on its hyperparameters and the Genetic Algorithm (GA) is a commonly used algorithm in determining the optimal hyperparameters for this model. In this study, we propose a flowchart algorithm to investigate the impact of GA parameters on the accuracy of XGBoost models over the hyperparameter grid for load forecasting. The maximum lo

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6

Yang, Hao, Jiaxi Li, Siru Liu, Xiaoling Yang, and Jialin Liu. "Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation." JMIR Medical Informatics 10, no. 6 (2022): e36958. http://dx.doi.org/10.2196/36958.

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Background Hypoglycemia is a common adverse event in the treatment of diabetes. To efficiently cope with hypoglycemia, effective hypoglycemia prediction models need to be developed. Objective The aim of this study was to develop and validate machine learning models to predict the risk of hypoglycemia in adult patients with type 2 diabetes. Methods We used the electronic health records of all adult patients with type 2 diabetes admitted to West China Hospital between November 2019 and December 2021. The prediction model was developed based on XGBoost and natural language processing. F1 score, a

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7

Gu, Kai, Jianqi Wang, Hong Qian, and Xiaoyan Su. "Study on Intelligent Diagnosis of Rotor Fault Causes with the PSO-XGBoost Algorithm." Mathematical Problems in Engineering 2021 (April 26, 2021): 1–17. http://dx.doi.org/10.1155/2021/9963146.

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On basis of fault categories detection, the diagnosis of rotor fault causes is proposed, which has great contributions to the field of intelligent operation and maintenance. To improve the diagnostic accuracy and practical efficiency, a hybrid model based on the particle swarm optimization-extreme gradient boosting algorithm, namely, PSO-XGBoost is designed. XGBoost is used as a classifier to diagnose rotor fault causes, having good performance due to the second-order Taylor expansion and the explicit regularization term. PSO is used to automatically optimize the process of adjusting the XGBoo

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8

Liu, Jialin, Jinfa Wu, Siru Liu, Mengdie Li, Kunchang Hu, and Ke Li. "Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model." PLOS ONE 16, no. 2 (2021): e0246306. http://dx.doi.org/10.1371/journal.pone.0246306.

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Purpose The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models. Methods We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), an

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9

Ji, Shouwen, Xiaojing Wang, Wenpeng Zhao, and Dong Guo. "An Application of a Three-Stage XGBoost-Based Model to Sales Forecasting of a Cross-Border E-Commerce Enterprise." Mathematical Problems in Engineering 2019 (September 16, 2019): 1–15. http://dx.doi.org/10.1155/2019/8503252.

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Sales forecasting is even more vital for supply chain management in e-commerce with a huge amount of transaction data generated every minute. In order to enhance the logistics service experience of customers and optimize inventory management, e-commerce enterprises focus more on improving the accuracy of sales prediction with machine learning algorithms. In this study, a C-A-XGBoost forecasting model is proposed taking sales features of commodities and tendency of data series into account, based on the XGBoost model. A C-XGBoost model is first established to forecast for each cluster of the re

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10

Sovia, Nabila Ayunda, Ni Wayan Surya Wardhani, Eni Sumarminingsih, and Elvo Ramadhan Shofa. "Enhancing Image Classification of Cabbage Plant Diseases Using a Hybrid Model Convolutional Neural Network and XGBoost." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 1 (2025): 278–89. https://doi.org/10.18860/cauchy.v10i1.30866.

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Classifying imbalanced datasets presents significant challenges, often leading to biased model performance, particularly in multiclass classification. This study addresses these issues by integrating Convolutional Neural Networks (CNN) and XGBoost, leveraging CNN’s exceptional feature extraction capabilities and XGBoost's robust handling of imbalanced data. The Hybrid CNN-XGBoost model was applied to classify cabbage plants affected by pests and diseases, which are categorized into five classes, with a significant imbalance between healthy and affected plants. The dataset, characterized by sev

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11

Wang, Yu, Li Guo, Yanrui Zhang, and Xinyue Ma. "Research on CSI 300 Stock Index Price Prediction Based On EMD-XGBoost." Frontiers in Computing and Intelligent Systems 3, no. 1 (2023): 72–77. http://dx.doi.org/10.54097/fcis.v3i1.6027.

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The combination of artificial intelligence techniques and quantitative investment has given birth to various types of price prediction models based on machine learning algorithms. In this study, we verify the applicability of machine learning fused with statistical method models through the EMD-XGBoost model for stock price prediction. In the modeling process, specific solutions are proposed for overfitting problems that arise. The stock prediction model of machine learning fused with statistical learning was constructed from an empirical perspective, and an XGBoost algorithm model based on em

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12

Suresh, Tamilarasi, Assegie Tsehay Admassu, Sangeetha Ganesan, Tulasi Ravulapalli Lakshmi, Radha Mothukuri, and Salau Ayodeji Olalekan. "Explainable extreme boosting model for breast cancer diagnosis." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5764–69. https://doi.org/10.11591/ijece.v13i5.pp5764-5769.

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This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains t

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13

Xiong, Shuai, Zhixiang Liu, Chendi Min, Ying Shi, Shuangxia Zhang, and Weijun Liu. "Compressive Strength Prediction of Cemented Backfill Containing Phosphate Tailings Using Extreme Gradient Boosting Optimized by Whale Optimization Algorithm." Materials 16, no. 1 (2022): 308. http://dx.doi.org/10.3390/ma16010308.

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Unconfined compressive strength (UCS) is the most significant mechanical index for cemented backfill, and it is mainly determined by traditional mechanical tests. This study optimized the extreme gradient boosting (XGBoost) model by utilizing the whale optimization algorithm (WOA) to construct a hybrid model for the UCS prediction of cemented backfill. The PT proportion, the OPC proportion, the FA proportion, the solid concentration, and the curing age were selected as input variables, and the UCS of the cemented PT backfill was selected as the output variable. The original XGBoost model, the

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14

Zhu, Yiming. "Stock Price Prediction based on LSTM and XGBoost Combination Model." Transactions on Computer Science and Intelligent Systems Research 1 (October 12, 2023): 94–109. http://dx.doi.org/10.62051/z6dere47.

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In recent years, many machine learning and deep learning algorithms have been applied to stock prediction, providing a reference basis for stock trading, and LSTM neural network and XGBoost algorithm are two typical representatives, each with advantages and disadvantages in prediction. In view of this, we propose a combination model based on LSTM and XGBoost, which combines the advantages of LSTM in processing time series data and the ability of XGBoost to evaluate the importance of features. The combination model first selects feature variables with high importance through XGBoost, performs d

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15

Gu, Zhongyuan, Miaocong Cao, Chunguang Wang, Na Yu, and Hongyu Qing. "Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model." Sustainability 14, no. 16 (2022): 10421. http://dx.doi.org/10.3390/su141610421.

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The extreme gradient boosting (XGBoost) ensemble learning algorithm excels in solving complex nonlinear relational problems. In order to accurately predict the surface subsidence caused by mining, this work introduces the genetic algorithm (GA) and XGBoost integrated algorithm model for mining subsidence prediction and uses the Python language to develop the GA-XGBoost combined model. The hyperparameter vector of XGBoost is optimized by a genetic algorithm to improve the prediction accuracy and reliability of the XGBoost model. Using some domestic mining subsidence data sets to conduct a model

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16

Kandi, Kianeh, and Antonio García-Dopico. "Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms." Machine Learning and Knowledge Extraction 7, no. 1 (2025): 20. https://doi.org/10.3390/make7010020.

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This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to understand sequential dependencies in transaction data. This research sheds light on which model is more effective in addressing the challenges posed by imbalanced datasets in credit risk assessment. The methodology utilized for imbalanced datasets includes the use of the Synthetic Minority Oversampli

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17

Harriz, Muhammad Alfathan, Nurhaliza Vania Akbariani, Harlis Setiyowati, and Handri Santoso. "Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction." Jambura Journal of Informatics 5, no. 1 (2023): 1–6. http://dx.doi.org/10.37905/jji.v5i1.18814.

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This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuabl

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18

Lee, Jong-Hyun, and In-Soo Lee. "Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost." Batteries 9, no. 11 (2023): 544. http://dx.doi.org/10.3390/batteries9110544.

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Lithium batteries have recently attracted significant attention as highly promising energy storage devices within the secondary battery industry. However, it is important to note that they may pose safety risks, including the potential for explosions during use. Therefore, achieving stable and safe utilization of these batteries necessitates accurate state-of-charge (SOC) estimation. In this study, we propose a hybrid model combining temporal convolutional network (TCN) and eXtreme gradient boosting (XGBoost) to investigate the nonlinear and evolving characteristics of batteries. The primary g

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19

Siringoringo, Rimbun, Resianta Perangin-angin, and Jamaluddin Jamaluddin. "MODEL HIBRID GENETIC-XGBOOST DAN PRINCIPAL COMPONENT ANALYSIS PADA SEGMENTASI DAN PERAMALAN PASAR." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 5, no. 2 (2021): 97–103. http://dx.doi.org/10.46880/jmika.vol5no2.pp97-103.

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Extreme Gradient Boosting(XGBoost) is a popular boosting algorithm based on decision trees. XGBoost is the best in the boosting group. XGBoost has excellent convergence. On the other hand, XGBoost is a Hyper parameterized model. Determining the value of each parameter is classified as difficult, resulting in the results obtained being trapped in the local optimum situation. Determining the value of each parameter manually, of course, takes a lot of time. In this study, a Genetic Algorithm (GA) is applied to find the optimal value of the XGBoost hyperparameter on the market segmentation problem

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20

Zeng, Shuang, Chang Liu, Heng Zhang, Baoqun Zhang, and Yutong Zhao. "Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model." Energies 18, no. 2 (2025): 227. https://doi.org/10.3390/en18020227.

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To tackle the challenges of limited accuracy and poor generalization in short-term load forecasting under complex nonlinear conditions, this study introduces a Prophet–BO–XGBoost-based forecasting framework. This approach employs the XGBoost model to interpret the nonlinear relationships between features and loads and integrates the Prophet model for label prediction from a time-series viewpoint. Given that hyperparameters substantially impact XGBoost’s performance, this study leverages Bayesian optimization (BO) to refine these parameters. Using a Gaussian process-based surrogate model and an

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21

Zhang, Kun. "Transmission Line Fault Diagnosis Method Based on SDA-ISSA-XGBoost under Meteorological Factors." Journal of Physics: Conference Series 2666, no. 1 (2023): 012006. http://dx.doi.org/10.1088/1742-6596/2666/1/012006.

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Abstract Transmission lines are directly exposed to the natural environment and are prone to failure due to meteorological factors. A novel approach for diagnosing transmission line faults under various meteorological conditions has been introduced. This method, known as SDA-ISSA-XGBoost, combines the power of Stacked Denoising Autoencoder (SDA), an improved Sparrow Search Algorithm (ISSA) enhanced with chaotic mapping sequences, adaptive weights, improved iterative local search, and a random differential mutation strategy, and eXtreme Gradient Boosting (XGBoost). The process begins with SDA,

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22

He, Wenwen, Hongli Le, and Pengcheng Du. "Stroke Prediction Model Based on XGBoost Algorithm." International Journal of Applied Sciences & Development 1 (December 13, 2022): 7–10. http://dx.doi.org/10.37394/232029.2022.1.2.

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In this paper, individual sample data randomly measured are preprocessed, for example, outliers values are deleted and the characteristics of the samples are normalized to between 0 and 1. The correlation analysis approach is then used to determine and rank the relevance of stroke characteristics, and factors with poor correlation are discarded. The samples are randomly split into a 70% training set and a 30% testing set. Finally,the random forest model and XGBoost algorithm combined with cross-validation and grid search method are implemented to learn the stroke characteristics. The accuracy

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23

Xiaobing Lin, Xiaobing Lin, Zhe Wu Xiaobing Lin, Jianfa Chen Zhe Wu, Lianfen Huang Jianfa Chen, and Zhiyuan Shi Lianfen Huang. "A Credit Scoring Model Based on Integrated Mixed Sampling and Ensemble Feature Selection: RBR_XGB." 網際網路技術學刊 23, no. 5 (2022): 1061–68. http://dx.doi.org/10.53106/160792642022092305014.

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<p>With the rapid development of the economy, financial institutions pay more and more attention to the importance of financial credit risk. The XGBoost algorithm is often used in credit scoring. However, it should be noted that XGBoost has three disadvantages when dealing with small samples of high-dimensional imbalance: (1) the model classification results are more biased towards the majority class when the XGBoost algorithm is used in training imbalanced data, this results in reduced model accuracy. (2) XGBoost algorithm is prone to overfitting in high-dimensional data because the hig

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24

Riando, Dhafin, and Afiyati Afiyati. "Implementasi Algoritma XGBoost untuk Memprediksi Harga Jual Cabai Rawit di DKI Jakarta." Eduvest - Journal of Universal Studies 4, no. 9 (2024): 7877–89. http://dx.doi.org/10.59188/eduvest.v4i9.3784.

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This research focuses on applying the XGBoost algorithm to analyze and predict cayenne pepper prices. The main aim is to exploit XGBoost's exceptional capability to manage large datasets and discern intricate patterns for precise price forecasting. The dataset comprises historical cayenne pepper price data, along with pertinent economic and climatic factors. The XGBoost model was developed and validated on this dataset, with its performance assessed using metrics. The results indicated a high level of accuracy, achieving an R² score of 99% on the training set and 92% on the test set, reflectin

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Zhang, Jun, Ranran Wang, Yijun Lu, and Jiandong Huang. "Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm." Buildings 14, no. 3 (2024): 591. http://dx.doi.org/10.3390/buildings14030591.

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Landscape geopolymer concrete (GePoCo) with environmentally friendly production methods not only has a stable structure but can also effectively reduce environmental damage. Nevertheless, GePoCo poses challenges with its intricate cementitious matrix and a vague mix design, where the components and their relative amounts can influence the compressive strength. In response to these challenges, the application of accurate and applicable soft computing techniques becomes imperative for predicting the strength of such a composite cementitious matrix. This research aimed to predict the compressive

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Guo, RuYan, MinFang Peng, ZhenQi Cao, and RunFu Zhou. "Transformer graded fault diagnosis based on neighborhood rough set and XGBoost." E3S Web of Conferences 243 (2021): 01002. http://dx.doi.org/10.1051/e3sconf/202124301002.

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Aiming at the uncertainty of fault type reasoning based on fault data in transformer fault diagnosis model, this paper proposed a hierarchical diagnosis model based on neighborhood rough set and XGBoost. The model used arctangent transformation to preprocess the DGA data, which could reduce the distribution span of data features and the complexity of model training. Using 5 characteristic gases and 16 gas ratios as the input characteristic parameters of the XGBoost model at all levels, reduction was performed on these 21 input feature attributes, features that had a high contribution to fault

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Xie, Peifeng, and Jinghang Xu. "Prediction of diabetes mellitus using XGBoost model." Applied and Computational Engineering 67, no. 1 (2024): 131–41. http://dx.doi.org/10.54254/2755-2721/78/20240646.

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With the number of health-threatening diseases and deaths on the rise, medical decision support systems continue to prove effective in improving the efficiency of physicians and other healthcare providers and supporting clinical decisions. Diabetes remains one of the leading diseases responsible for many deaths worldwide. Diabetes is characterized by elevated blood glucose levels, which can have serious consequences for other organs in the body. According to the International Diabetes Alliance (IDA), 382 million people currently have diabetes and this number is expected to double to 592 millio

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Xie, Peifeng, and Jinghang Xu. "Prediction of diabetes mellitus using XGBoost model." Applied and Computational Engineering 67, no. 1 (2024): 131–41. http://dx.doi.org/10.54254/2755-2721/67/20240646.

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With the number of health-threatening diseases and deaths on the rise, medical decision support systems continue to prove effective in improving the efficiency of physicians and other healthcare providers and supporting clinical decisions. Diabetes remains one of the leading diseases responsible for many deaths worldwide. Diabetes is characterized by elevated blood glucose levels, which can have serious consequences for other organs in the body. According to the International Diabetes Alliance (IDA), 382 million people currently have diabetes and this number is expected to double to 592 millio

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Ogunleye, Adeola, and Qing-Guo Wang. "XGBoost Model for Chronic Kidney Disease Diagnosis." IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, no. 6 (2020): 2131–40. http://dx.doi.org/10.1109/tcbb.2019.2911071.

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Yin, Yilan, Yanguang Sun, Feng Zhao, and Jinxiang Chen. "Improved XGBoost model based on genetic algorithm." International Journal of Computer Applications in Technology 62, no. 3 (2020): 240. http://dx.doi.org/10.1504/ijcat.2020.10028423.

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Chen, Jinxiang, Feng Zhao, Yanguang Sun, and Yilan Yin. "Improved XGBoost model based on genetic algorithm." International Journal of Computer Applications in Technology 62, no. 3 (2020): 240. http://dx.doi.org/10.1504/ijcat.2020.106571.

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Zong, Zihe. "Credit Risk Assessment Model Based on XGBoost." Advances in Economics, Management and Political Sciences 193, no. 1 (2025): 170–79. https://doi.org/10.54254/2754-1169/2025.lh24569.

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Against the backdrop of uncertainty increasing global economic, Credit risk has become one of the core risks faced by financial institutions, enterprises and individuals. This study focuses on credit risk assessment in the field of financial risk and introduces machine learning models to address issues such as poor model interpretability and imbalanced data in existing models. By processing the German credit dataset in various aspects, including handling missing values, label encoding, standardization, data balancing, and analyzing the basic characteristics of the data to discover the advantag

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Kumar, Thella Ajay. "Stroke Prediction Using XGboost and a Fusion of XGboost with Random Forest." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48840.

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Abstract - Stroke is a life-threatening medical condition caused by disrupted blood flow to the brain, representing a major global health concern with significant health and economic consequences. Researchers are working to tackle this challenge by developing automated stroke prediction algorithms, which can enable timely interventions and potentially save lives. As the global population ages, the risk of stroke increases, making the need for accurate and reliable prediction systems more critical. In this study, we evaluate the performance of an advanced machine learning (ML) approach, focusin

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Zhao, Haolei, Yixian Wang, Xian Li, Panpan Guo, and Hang Lin. "Prediction of Maximum Tunnel Uplift Caused by Overlying Excavation Using XGBoost Algorithm with Bayesian Optimization." Applied Sciences 13, no. 17 (2023): 9726. http://dx.doi.org/10.3390/app13179726.

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The uplifting behaviors of existing tunnels due to overlying excavations are complex and non-linear. They are contributed to by multiple factors, and therefore, they are difficult to be accurately predicted. To address this issue, an extreme gradient boosting (XGBoost) prediction model based on Bayesian optimization (BO), namely, BO-XGBoost, was developed specifically for assessing the tunnel uplift. The modified model incorporated various factors such as an engineering design, soil types, and site construction conditions as input parameters. The performance of the BO-XGBoost model was compare

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35

Yahya, Furqon Nurbaril, Mochammad Anshori, and Ahsanun Naseh Khudori. "Evaluasi Performa XGBoost dengan Oversampling dan Hyperparameter Tuning untuk Prediksi Alzheimer." Techno.Com 24, no. 1 (2025): 1–12. https://doi.org/10.62411/tc.v24i1.12057.

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Alzheimer adalah gangguan neurodegeneratif yang mempengaruhi kemampuan kognitif dan memori, deteksi dini sangat penting untuk pengobatan yang tepat. Namun, untuk mendeteksi Alzheimer memerlukan biaya yang tinggi, sehingga penggunaan machine learning bisa menjadi alternatif yang lebih efisien. Salah satu tantangan utama dalam penerapan machine learning untuk mendeteksi Alzheimer adalah ketidakseimbangan data, di mana jumlah kasus positif (Alzheimer) jauh lebih sedikit daripada kasus negatif (sehat), yang berdampak pada kinerja model. Penelitian ini bertujuan untuk mengidentifikasi pengaruh tekn

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Xu, Bing, Youcheng Tan, Weibang Sun, Tianxing Ma, Hengyu Liu, and Daguo Wang. "Study on the Prediction of the Uniaxial Compressive Strength of Rock Based on the SSA-XGBoost Model." Sustainability 15, no. 6 (2023): 5201. http://dx.doi.org/10.3390/su15065201.

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The uniaxial compressive strength of rock is one of the important parameters characterizing the properties of rock masses in geotechnical engineering. To quickly and accurately predict the uniaxial compressive strength of rock, a new SSA-XGBoost optimizer prediction model was produced to predict the uniaxial compressive strength of 290 rock samples. With four parameters, namely, porosity (n,%), Schmidt rebound number (Rn), longitudinal wave velocity (Vp, m/s), and point load strength (Is(50), MPa) as input variables and uniaxial compressive strength (UCS, MPa) as the output variables, a predic

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N., KALAISELVI, and SASIKALA R. "ENHANCED LIVER CANCER DETECTION USING HYBRID CNN-XGBOOST MODEL IN MACHINE LEARNING." Journal of Dynamics and Control 9, no. 3 (2025): 109–15. https://doi.org/10.71058/jodac.v9i3008.

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Since liver cancer is such an aggressive illness, improving patient outcomes requires early identification. This paper presents a novel detection technique that blends Extreme Gradient Boosting (XGBoost) with Convolutional Neural Networks (CNNs). Medical image complex feature extraction is a strong suit for CNNs, and XGBoost is a potent classifier that excels at processing high-dimensional data. The suggested hybrid model attempts to improve detection accuracy through integrating the deep feature extraction powers of CNNs with the effective classification of XGBoost. Based on experimental data

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Choi, Bong-Jin, Seong-Woo Lee, and Yeonkook J. Kim. "Developing Consumer Bank Loan Delinquency Prediction Model Using XAI." Korean Association Of Computers And Accounting 22, no. 2 (2024): 43–61. http://dx.doi.org/10.32956/kaoca.2024.22.2.43.

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[Purpose] This study aims to create a loan default prediction model using machine learning, a core concept of AI technology, and explainable AI (XAI) techniques, and to compare and evaluate it against highly interpretable models such as logistic regression. [Methodology] This study utilizes the data of household loan customers of Local Bank A in South Korea from December 2020 to June 2022 to generate and compare the logistic regression model and the XGBoost model. We used SHAP, one of the XAI techniques, to explain the XGBoost model. [Findings] The XGBoost model shows better prediction perform

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Lin, Nan, Jiawei Fu, Ranzhe Jiang, Genjun Li, and Qian Yang. "Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm." Remote Sensing 15, no. 15 (2023): 3764. http://dx.doi.org/10.3390/rs15153764.

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Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provi

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Wu, Kehe, Yanyu Chai, Xiaoliang Zhang, and Xun Zhao. "Research on Power Price Forecasting Based on PSO-XGBoost." Electronics 11, no. 22 (2022): 3763. http://dx.doi.org/10.3390/electronics11223763.

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With the reform of the power system, the prediction of power market pricing has become one of the key problems that needs to be solved in time. Power price prediction plays an important role in maximizing the profits of the participants in the power market and making full use of power energy. In order to improve the prediction accuracy of the power price, this paper proposes a power price prediction method based on PSO optimization of the XGBoost model, which optimizes eight main parameters of the XGBoost model through particle swarm optimization to improve the prediction accuracy of the XGBoo

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Feng, Dachun, Bing Zhou, Shahbaz Gul Hassan, et al. "A Hybrid Model for Temperature Prediction in a Sheep House." Animals 12, no. 20 (2022): 2806. http://dx.doi.org/10.3390/ani12202806.

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Too high or too low temperature in the sheep house will directly threaten the healthy growth of sheep. Prediction and early warning of temperature changes is an important measure to ensure the healthy growth of sheep. Aiming at the randomness and empirical problem of parameter selection of the traditional single extreme Gradient boosting (XGBoost) model, this paper proposes an optimization method based on Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO). Then, using the proposed PCA-PSO-XGBoost to predict the temperature in the sheep house. First, PCA is used to screen

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42

Zheng, Jiayan, Tianchen Yao, Jianhong Yue, Minghui Wang, and Shuangchen Xia. "Compressive Strength Prediction of BFRC Based on a Novel Hybrid Machine Learning Model." Buildings 13, no. 8 (2023): 1934. http://dx.doi.org/10.3390/buildings13081934.

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Basalt fiber-reinforced concrete (BFRC) represents a form of high-performance concrete. In structural design, a 28-day resting period is required to achieve compressive strength. This study extended an extreme gradient boosting tree (XGBoost) hybrid model by incorporating genetic algorithm (GA) optimization, named GA-XGBoost, for the projection of compressive strength (CS) on BFRC. GA optimization may reduce many debugging efforts and provide optimal parameter combinations for machine learning (ML) algorithms. The XGBoost is a powerful integrated learning algorithm with efficient, accurate, an

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Singh, Puneet, Shubha Mishra, Gargi Porwal, Prakhar Saxena, and Rishabh Tripathi. "Credit Risk Model: Research on Credit Risk Categorization model using XGBoost." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43005.

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Machine Learning is a subset of Artificial Intelligence technology that enables systems to learn and make decisions on their own. These systems can make accurate decisions by analyzing datasets and information without the need for explicit programming. This paper mainly introduces the application of machine learning algorithm (XGBoost) in credit risk assessment in the financial industry. Credit risk assessment is a significant challenge for banks to assess credit worthiness among many applicants and plays a very crucial role in the profitability of banks. Our research paper addresses the limit

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44

Wang, Li-Jing, Zhi-Ying Liu, Fei Li, Kang-Kang Tan, Yang Han, and Ai-Min Yang. "Sparrow-based optimised XGBoost blast furnace utilisation factor forecasting model." Ironmaking & Steelmaking: Processes, Products and Applications 51, no. 2 (2024): 107–16. http://dx.doi.org/10.1177/03019233231215197.

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An XGBoost regression blast furnace utilisation coefficient forecasting model based on sparrow optimisation is constructed. The model is to improve the blast furnace utilisation coefficient. To combine the advantages of sparrow-optimised XGBoost in terms of multiple linearity and regression, etc., and based on 652 sets of original sample information, we regress the blast furnace utilisation coefficient forecasting model with the help of sparrow-optimised XGBoost. The relationship between the regulation of economic and technical indicators (coke ratio, coal ratio, fuel ratio, etc.) and the blas

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45

Liu, Baohua, Hang Lin, Yifan Chen, and Chaoyi Yang. "Prediction of Rock Unloading Strength Based on PSO-XGBoost Hybrid Models." Materials 17, no. 17 (2024): 4214. http://dx.doi.org/10.3390/ma17174214.

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Rock excavation is essentially an unloading behavior, and its mechanical properties are significantly different from those under loading conditions. In response to the current deficiencies in the peak strength prediction of rocks under unloading conditions, this study proposes a hybrid learning model for the intelligent prediction of the unloading strength of rocks using simple parameters in rock unloading tests. The XGBoost technique was used to construct a model, and the PSO-XGBoost hybrid model was developed by employing particle swarm optimization (PSO) to refine the XGBoost parameters for

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Ha, Jinbing, and Ziyi Zhou. "Subway Energy Consumption Prediction based on XGBoost Model." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 548–52. http://dx.doi.org/10.54097/hset.v70i.13958.

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In the process of urban rail transit operation and management, accurate prediction of subway energy consumption is beneficial for establishing a reasonable operational organization mode and evaluating energy efficiency. Due to the multitude of factors affecting train energy consumption, traditional mathematical regression methods struggle to guarantee predictive accuracy. Thus, a energy consumption prediction method based on XGBoost is proposed. To enhance model training efficiency and accuracy, the Lasso model is utilized for feature selection of subway energy consumption influencing factors.

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Wan, Zhi, Yading Xu, and Branko Šavija. "On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance." Materials 14, no. 4 (2021): 713. http://dx.doi.org/10.3390/ma14040713.

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Compressive strength is the most significant metric to evaluate the mechanical properties of concrete. Machine Learning (ML) methods have shown promising results for predicting compressive strength of concrete. However, at present, no in-depth studies have been devoted to the influence of dimensionality reduction on the performance of different ML models for this application. In this work, four representative ML models, i.e., Linear Regression (LR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are trained and used to predict the com

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Yuan, Jianming. "Predicting Death Risk of COVID-19 Patients Leveraging Machine Learning Algorithm." Applied and Computational Engineering 8, no. 1 (2023): 186–90. http://dx.doi.org/10.54254/2755-2721/8/20230122.

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The first instance of COVID-19 was found in Wuhan, China, which mainly caused damage to human body in the form of respiratory diseases. In this study, an XGBoost prediction model was put forward according to the analysis on age, pneumonia, diabetes, and other attributes in the dataset, which was employed to estimate the COVID-19 patients' risk of death. In this study, a lot of preprocessing was carried out on the dataset, such as deleting null values in the dataset. In addition, there are strong correlation between sex, pnueumonia and death probability. In this study, XGBoost, CatBoost, logist

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Andrew, O. H., B. D. Oluyemi-Ayibiowu, O. T. Biala, Y. B. Oluwadiya, J. O. Ohwofasa, and O. D. Titiloye. "Evaluation of Load-Bearing Capacity of Lateritic Soils under Unsoaked and Soaked Conditions Commonly Employed in Pavement Design and Construction in Akure, Ondo State, Nigeria." Journal of Applied Sciences and Environmental Management 29, no. 6 (2025): 1951–60. https://doi.org/10.4314/jasem.v29i6.26.

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The objective of this paper was to evaluate the Load-Bearing Capacity of Lateritic Soils under Unsoaked and Soaked Conditions Commonly Employed in Pavement Design and Construction in Akure, Ondo State, Nigeria, using CatBoost and XGBoost . Results obtained show that for the Unsoaked CBR, the CatBoost model achieves an R-squared of 0.78, while XGBoost outperforms it with an R-squared of 0.94. For the Soaked CBR, CatBoost reaches an R-squared of 0.67, and XGBoost achieves 0.77. These results demonstrate the models' strong ability to explain the variance in CBR values. Feature importance analysis

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Ubaidillah, Rahmad, Muliadi Muliadi, Dodon Turianto Nugrahadi, M. Reza Faisal, and Rudy Herteno. "Implementasi XGBoost Pada Keseimbangan Liver Patient Dataset dengan SMOTE dan Hyperparameter Tuning Bayesian Search." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 3 (2022): 1723. http://dx.doi.org/10.30865/mib.v6i3.4146.

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Liver disease is a disorder of liver function caused by infection with viruses, bacteria or other toxic substances so that the liver cannot function properly. This liver disease needs to be diagnosed early using a classification algorithm. By using the Indian liver patient dataset, predictions can be made using a classification algorithm to determine whether or not patients have liver disease. However, this dataset has a problem where there is an imbalance of data between patients with liver disease and those without, so it can reduce the performance of the prediction model because it tends to

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